Letters

The findings of this study suggest that mortality measures could be enhanced by taking into account patient preferences for treatment and end-of-life care. Mihaela S. Stefan, MD Randa Jaber, MD Peter K. Lindenauer, MD, MSc Jane L. Garb, MS Janet Fitzgerald, RN, MS Michael B. Rothberg, MD, MPH Author Affiliations: Center for Quality of Care Research, Baystate Medical Center, Springfield, Massachusetts (Stefan, Lindenauer); Department of Medicine, Baystate Medical Center, Springfield, Massachusetts (Stefan, Jaber, Fitzgerald); Department of Epidemiology and Biostatistics, Baystate Medical Center, Springfield, Massachusetts (Garb); Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Rothberg). Corresponding Author: Mihaela S. Stefan, MD, Department of Medicine, Baystate Medical Center, 759 Chestnut St, Springfield, MA 01199 (mihaela [email protected]). Published Online: March 16, 2015. doi:10.1001/jamainternmed.2015.114. Author Contributions: Dr Stefan had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: All authors. Acquisition, analysis, or interpretation of data: Stefan, Jaber, Garb, Fitzgerald, Rothberg. Drafting of the manuscript: Stefan, Jaber, Garb. Critical revision of the manuscript for important intellectual content: Stefan, Lindenauer, Garb, Fitzgerald, Rothberg. Statstical analysis: Stefan, Garb. Administrative, technical, or material support: Jaber, Fitzgerald. Study supervision: Jaber, Lindenauer, Fitzgerald, Rothberg. Conflict of Interest Disclosures: Dr Lindenauer receives support to develop hospital outcome measures from the Centers for Medicare & Medicaid Services. No other conflicts were reported. Funding/Support: Dr Stefan is supported by grant 1K01HL114631-01A1 from the National Heart, Lung, and Blood Institute of the National Institutes of Health and by grant UL1RR025752 from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health. Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclaimer: The content of this article is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health. Additional Contributions: Anu Joshi, Clinical Research Coordinator, Center for Quality of Care Research, Baystate Medical Center, provided assistance in editing the manuscript and preparing the tables. Ms Joshi was not compensated for her contribution. 1. US Department of Health and Human Services, Centers for Medicare & Medicaid Services. Hospital Value-Based Purchasing Program. Report No. ICN 907664. http://www.cms.gov/Outreach-and-Education/Medicare-Learning -Network-MLN/MLNProducts/downloads/Hospital_VBPurchasing_Fact_Sheet _ICN907664.pdf. Published March 2013. Accessed September 2, 2014. 2. Kupfer JM. The morality of using mortality as a financial incentive: unintended consequences and implications for acute hospital care. JAMA. 2013; 309(21):2213-2214. 3. Lindenauer PK, Bernheim SM, Grady JN, et al. The performance of US hospitals as reflected in risk-standardized 30-day mortality and readmission rates for Medicare beneficiaries with pneumonia. J Hosp Med. 2010;5(6):12-18. 4. Mortensen EM, Coley CM, Singer DE, et al. Causes of death for patients with community-acquired pneumonia: results from the Pneumonia Patient Outcomes Research Team cohort study. Arch Intern Med. 2002;162(9): 1059-1064.

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5. Weissman DE, Meier DE. Identifying patients in need of a palliative care assessment in the hospital setting: a consensus report from the Center to Advance Palliative Care. J Palliat Med. 2011;14(1):17-23. 6. Lilford R, Pronovost P. Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ. 2010;340:c2016.

Editor's Note

Valuing Our Patients’ Preferences It makes sense to pay for value. By paying hospitals and physicians more for higher-value outcomes rather than more for doing more things, we are likely to improve the health of our patients as well as the financial health of our system. The challenge is identifying measurable outcomes that represent highvalue health care. On the surface, it makes sense to reward hospitals that have lower mortality rates for a given condition, such as pneumonia. But, as shown by Stefan and colleagues,1 issues in health policy are often more complicated than they initially appear. The authors found that pneumonia played a major role in the death of only 18.3% of patients. For 81.7% of patients, pneumonia was a minor contributor in that the patient had “advanced life-threatening illnesses (ie, met criteria for palliative care) and pneumonia was on the final pathway to death.”1 Indeed, 57.6% of these patients had a do-not-resuscitate or donot-intubate order on admission. For these patients, the usual process measures for pneumonia treatment would not likely be relevant. Instead, the most patient-centered outcomes, and the ones we would want the system to focus on, would likely be end-of-life planning, pain control, and spiritual counseling. Paying hospitals for lower mortality rates could create a perverse incentive for hospitals to aggressively treat elderly patients with multiple comorbidities, even if the patients’ wishes were for more of a focus on comfort. None of this is to detract from the importance of measuring and rewarding quality. We simply have to work on deriving measures that better take into account the wishes of our patients. Mitchell H. Katz, MD Conflict of Interest Disclosures: None reported. 1. Stefan MS, Jaber R, Lindenauer PK, Garb JL, Fitzgerald J, Rothberg MB. Death among patients hospitalized with pneumonia: implications for hospital outcome measures [published online March 16, 2015]. JAMA Intern Med. doi:10.1001 /jamainternmed.2015.114.

The Usefulness of Diagnostic Testing in the Initial Evaluation of Chronic Kidney Disease Chronic kidney disease (CKD) affects approximately 13% of adults in the United States and is associated with significant morbidity, mortality, and costs.1-3 There is a broad differential for CKD, including diabetes mellitus, hypertension, glomerulonephritis, tubulointerstitial disease, urologic causes, and unknown causes.2 To our knowledge, a comprehensive assessment of the tests used in CKD evaluation has not been conducted. We determined how often laboratory and imaging tests were obtained in the initial evaluation of CKD and whether these tests affected diagnosis and/or management. (Reprinted) JAMA Internal Medicine May 2015 Volume 175, Number 5

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Table 1. Patient Demographics and Clinical Characteristics Characteristic Male sex Age, median (IQR), y Married or with partner English speaking

No. (%) 914 (61.4) 70 (61-79) 845 (56.8) 1402 (94.3)

Race White

1084 (72.9)

African American

189 (12.7)

Hispanic

149 (10.0)

Other

65 (4.4)

Comorbidities Hypertension

1175 (79.0)

Diabetes mellitus

868 (58.4)

Coronary artery disease

382 (25.7)

History of cancer

359 (24.1)

Gout

207 (13.9)

Anemia

182 (12.2)

Obesity

164 (11.0)

Congestive heart failure

142 (9.6)

Benign prostatic hypertrophy

139 (9.3)

Kidney stones

130 (8.7)

Connective tissue disease

60 (4.0)

Nephrectomy

44 (3.0)

Monoclonal disease

41 (2.8)

Lupus erythematosus

18 (1.2)

History of hydronephrosis

18 (1.2)

History of renal artery stenosis Proteinuriaa

13 (0.9) 625 (42.0)

CKD stageb 1 or 2

183 (12.3)

3a

427 (28.7)

3b

589 (39.5)

4

276 (18.6)

5

12 (0.8)

Medications before initial visit Statin

865 (58.2)

β-Blocker

810 (54.5)

ACE inhibitor

608 (40.9)

Calcium channel blocker

531 (35.7)

Proton pump inhibitor

426 (28.7)

Thiazide diuretic

378 (25.4)

Loop diuretic

307 (20.6)

Angiotensin receptor blocker

306 (20.6)

Vitamin D supplement

245 (16.5)

Nonsteroidal anti-inflammatory drugs

203 (13.7)

Allopurinol

147 (9.9)

Renal replacement during study Dialysis Transplant

40 (2.7) 4 (0.3)

Abbreviations: ACE, angiotensin-converting enzyme; CKD, chronic kidney disease; IQR, interquartile range. a Urine dipstick for protein result of 1+ or greater, microalbuminuria (ⱖ30 mg/g), urine protein to creatinine ratio greater than 0.2. b Based on most recent estimated glomerular filtration (eGFR) rate before study enrollment period. CKD stage 1-2, eGFR, greater than 60 mL/min/1.73 m2; stage 3a eGFR, 45 to 59 mL/min/1.73 m2; stage 3b eGFR, 30 to 44 mL/min/1.73 m2; stage 4 eGFR, 15 to 29 mL/min/1.73 m2; and stage 5 eGFR, less than 15 mL/min/1.73 m2.

854

Methods | We conducted a retrospective cohort study of patients referred for initial evaluation of CKD from January 1, 2010, to January 1, 2013, to nephrology clinics affiliated with Brigham and Women’s Hospital and Massachusetts General Hospital in Boston, Massachusetts; 1487 patients were included (Table 1). Partners Institutional Review Board approved the study and waived the need for informed consent. Electronic medical records were abstracted. We used methods to ensure the validity and reliability of data, including review of 10 initial medical records by 2 of us (M.L.M. and S.S.W.) to refine criteria.4 Tests obtained at another clinic before the nephrology clinic visit were documented. We reviewed nephrology progress notes to ascertain the presumed cause of CKD and whether a test had been documented to affect the diagnosis and/or management. A test was considered to have affected diagnosis and/or management if it was specifically stated to have contributed to, confirmed, or established the underlying diagnosis of and/or any management decision related to CKD. This definition included documentation of negative and positive test results and diagnoses related to CKD. A second reviewer (E.R.) blindly abstracted a random sample of 36 patients’ records (2.4% of patients). The degree of interrater agreement, assessed by the prevalence-adjusted, bias-adjusted κ statistic,5,6 was a mean (SE) of 0.89 (0.02). Results | Among the 1487 patients included, common comorbidities were hypertension (79.0%) and diabetes (58.4%), and CKD stages were 3b (39.5%) and 3a (28.7%) (Table 1). Frequently obtained tests included measurement of calcium (94.8%), hemoglobin (84.0%), phosphate (83.5%), urine sediment (74.8%), and parathyroid hormone (74.1%) levels; urine dipstick for blood (69.9%) and protein (69.7%); serum protein electrophoresis (68.1%); and renal ultrasonography (67.7%) (Table 2). Determination of the hemoglobin A1c level, urine total protein to creatinine ratio, and urine microalbumin to creatinine ratio had relatively high yields, affecting diagnosis in 15.4%, 14.1%, and 13.0% of the patients and management in 10.1%, 13.7%, and 13.3%, respectively. Serum protein electrophoresis and renal ultrasonography, although frequently performed, had much lower yields, affecting diagnosis in 1.4% and 5.9% and management in 1.7% and 3.3% of the patients, respectively. Results of tests to detect antineutrophil cytoplasmic antibody and antiglomerular basement membrane antibody did not affect the diagnosis or management in any patients. Discussion | In this analysis of patients undergoing initial evaluation of CKD, we found that many tests are obtained frequently despite low rates of effect on diagnosis and management. Certain tests, such as serum protein electrophoresis and screening for antinuclear antibody, C3, C4, hepatitis C, hepatitis B, and antineutrophil cytoplasmic antibody, were obtained often (13.4%-68.1%) despite infrequently affecting diagnosis or management (0-1.7%). In contrast, hemoglobin A1c and urine protein quantification tests affected the diagnosis and management in 13.0% to 15.4% of the patients. These findings are limited by the retrospective study design, subjective nature of evaluating clinical usefulness, potential underesti-

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Table 2. Frequency and Yield of Diagnostic Testing Obtained in the Initial Evaluation of CKD No. (%)a Test Obtained

Frequency (N = 1487)

Abnormal Resultsb

Affected Diagnosisc

Affected Managementd

Primarily for Diagnosis Urine Sediment

1112 (74.8)

104 (9.4)

39 (3.5)

Dipstick for protein

1036 (69.7)

356 (34.4)

25 (2.4)

37 (3.3) 23 (2.2)

Dipstick for blood

1039 (69.9)

159 (15.3)

19 (1.8)

22 (2.1) 17 (1.7)

SPEP

1012 (68.1)

84 (8.3)

14 (1.4)

Renal ultrasonography

1007 (67.7)

270 (26.8)

59 (5.9)

33 (3.3)

Urine microalbumin to creatinine ratio

901 (60.6)

494 (54.8)

117 (13.0)

120 (13.3)

Urine total protein to creatinine ratio

811 (54.5)

415 (54.8)

114 (14.1)

111 (13.7)

UPEP

526 (35.4)

23 (4.4)

6 (1.1)

8 (1.5)

ANA

423 (28.5)

218 (51.5)

4 (0.9)

5 (1.2)

Uric acid

390 (26.2)

172 (44.1)

12 (3.3)

38 (9.7)

Serum-free light chains

374 (25.2)

168 (44.9)

5 (1.3)

8 (2.2)

C3

360 (24.2)

25 (6.9)

5 (1.4)

5 (1.4)

C4

359 (24.1)

29 (8.1)

4 (1.1)

4 (1.1)

HBVe

262 (17.6)

1 (0.4)

1 (0.4)

1 (0.4)

HCV

259 (17.4)

3 (1.2)

2 (0.8)

2 (0.8)

ANCA

205 (13.8)

5 (2.4)

0

0

Hemoglobin A1c

188 (12.6)

72 (38.3)

29 (15.4)

Rheumatoid factor

156 (10.5)

19 (12.2)

1 (0.6)

3 (1.9)

DsDNA

128 (8.6)

9 (7.0)

1 (0.8)

2 (1.6)

Anti-Ro antibody

77 (5.2)

8 (10.4)

2 (2.6)

4 (5.2)

Anti-La antibody

77 (5.2)

5 (6.5)

2 (2.6)

4 (5.2)

Cryoglobulins

74 (5.0)

3 (4.1)

4 (5.4)

4 (5.4)

Kidney biopsy

70 (4.7)

70 (100)

70 (100)

70 (100)

Anti-GBM

52 (3.6)

Abdominal CT

33 (2.2)

18 (55.5)

11 (33.3)

6 (18.2)

Creatine kinase

30 (2.0)

7 (23.3)

1 (3.3)

1 (3.3)

0

0

a

The denominator for the percentages provided is the number of tests ordered.

b

Defined for most laboratories based on the reference range established by the laboratory. For urine sediment, any finding other than an acellular sediment or epithelial cells was considered to be abnormal. For SPEP, UPEP, and serum-free light chains, any abnormal immunoglobulin finding was considered to be abnormal. For parathyroid hormone, Kidney Disease Outcomes Quality Initiative target plasma levels based on CKD stage were used to define abnormal laboratory values.2 An abnormal finding for imaging was defined as any abnormality documented in the final report, with the exception of simple cysts and nonobstructive stones for renal ultrasonography.

c

Defined as any test result that was noted in the nephrology progress notes to have contributed to, confirmed, or established any diagnosis.

d

Defined as any test result that were noted in the nephrology progress notes to have contributed to any management decision.

e

It is recommended that patients with advanced CKD (ⱖstage 4) receive hepatitis B vaccination before dialysis is initiated, and it is possible that some of these patients had hepatitis B serology tests performed for that reason; the serology tests were performed in 44 patients with CKD stage 4 and in 2 patients with CKD stage 5.

19 (10.1)

0

Renal nuclear scan

24 (1.6)

22 (91.7)

16 (66.7)

8 (33.3)

LDH

19 (1.3)

12 (63.2)

1 (5.3)

2 (10.5)

Haptoglobin

15 (1.0)

4 (26.7)

1 (6.7)

3 (20)

Antiphospholipid antibody

12 (0.8)

4 (33.3)

1 (8.3)

2 (16.7)

6 (0.4)

0

0

0

MRI

4 (0.3)

3 (75)

0

0

MRA

4 (0.3)

0

0

0

8 (0.6)

HIV

Abbreviations: ANA, antinuclear antibody; ANCA, antineutrophil cytoplasmic antibody; Anti-GBM, antiglomerular basement membrane antibody; CKD, chronic kidney disease; CT, computed tomography; DsDNA, double-stranded DNA; HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; LDH, lactate dehydrogenase; LDL-C, low-density lipoprotein cholesterol; MRA, magnetic resonance angiogram; MRI, magnetic resonance imaging; SPEP, serum protein electrophoresis; UPEP, urine protein electrophoresis.

Abdominal

Primarily for Management Calcium

1410 (94.8)

123 (8.7)

5 (0.4)

Hemoglobin

1249 (84.0)

373 (29.9)

0

90 (7.2)

Phosphate

1242 (83.5)

214 (17.2)

3 (0.2)

19 (1.5)

Parathyroid hormone

1102 (74.1)

619 (56.2)

0

97 (15.7)

25-Hydroxyvitamin D

817 (54.9)

352 (43.1)

0

119 (14.6)

Iron

551 (37.1)

52 (9.4)

0 (0.2)

84 (15.2)

LDL-C

163 (11.0)

65 (39.9)

0

11 (6.7)

mation of the benefit of negative test results, and representation from only 2 academic medical centers in the northeastern United States. Further investigation incorporating community-based patients and identifying subgroups benefiting from more extensive evaluation is needed. However, jamainternalmedicine.com

this study suggests that reflexively ordering several tests for CKD evaluation and management may be unnecessary. An evidence-based, targeted approach based on pretest probabilities of disease for diagnosis and management may be more efficient and reduce costs. (Reprinted) JAMA Internal Medicine May 2015 Volume 175, Number 5

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855

Letters

Mallika L. Mendu, MD, MBA Andrew Lundquist, MD Ayal A. Aizer, MD, MHS David E. Leaf, MD, MMSc Emily Robinson, MD, MPH David J. R. Steele, MD Sushrut S. Waikar, MD, MPH

ing physicians and advanced practice providers), and nurses with immediate (real-time) release of test results and other EHR information through a patient portal.

Author Affiliations: Division of Renal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Mendu, Leaf, Robinson, Waikar); Nephrology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (Lundquist, Steele); Harvard Radiation Oncology Program, Boston, Massachusetts (Aizer). Corresponding Author: Mallika L. Mendu, MD, MBA, Division of Renal Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, MRB-4, Boston, MA 02115 ([email protected]). Published Online: March 2, 2015. doi:10.1001/jamainternmed.2015.17. Author Contributions: Drs Mendu and Waikar had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Mendu, Aizer, Steele, Waikar. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Steele, Mendu. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Mendu, Aizer. Administrative, technical, or material support: Mendu, Leaf, Waikar. Study supervision: Robinson, Steele, Waikar. Conflict of Interest Disclosures: Dr Waikar served as a consultant to Abbvie, CVS Caremark, Harvard Clinical Research Institute, and Takeda; provided expert testimony or consultation for litigation related to nephrogenic systemic fibrosis (GE Healthcare) and mercury exposure; and has received grants from the National Institute of Diabetes and Digestive Kidney Diseases, Genzyme, Merck, Otsuka, Pfizer, and Satellite Healthcare. No other conflicts are reported. 1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038-2047. 2. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2)(suppl 1):S1-S266. 3. US Renal Data System. Atlas of Chronic Kidney Disease in the United States. Bethesda, MD: National Institutes of Health, National Institutes of Diabetes and Digestive and Kidney Diseases; 2010. US Renal Data System 2010 Annual Data Report; vol 1. http://www.usrds.org/2010/pdf/v1_00a_intros.PDF. Accessed October 15, 2014. 4. Gilbert EH, Lowenstein SR, Koziol-McLain J, Barta DC, Steiner J. Chart reviews in emergency medicine research: where are the methods? Ann Emerg Med. 1996;27(3):305-308. 5. Byrt T, Bishop J, Carlin JB. Bias, prevalence and kappa. J Clin Epidemiol. 1993; 46(5):423-429. 6. Van Ness PH, Towle VR, Juthani-Mehta M. Testing measurement reliability in older populations: methods for informed discrimination in instrument selection and application. J Aging Health. 2008;20(2):183-197.

Patient Access to Electronic Health Records During Hospitalization In 2001, the Institute of Medicine1 recommended improving patient engagement by providing continuous care, allowing patients to be the source of control and fostering transparency with patients and families. Electronic health records (EHRs) facilitate these objectives via the use of patient portals.2 Giving outpatients direct access to their health information helps clinicians find errors and improves patient satisfaction, although the implications of this type of access have not been well studied in the inpatient setting.3-5 This hospital-based study evaluates the experiences of patients, clinicians (includ856

Methods | This prospective cohort study was performed on a medical unit of the University of Colorado Hospital, Aurora, a 412-bed academic tertiary care hospital, from October 1, 2012, through March 31, 2013. Approval was obtained from the Colorado Multiple Institutional Review Board and the University of Colorado Hospital Research Review Committee. Participants provided oral informed consent, and all data were deidentified. Participants included hospital clinicians, nurses, and patients. Patient participants were enrolled by convenience sampling and used a study-provided electronic tablet to access parts of their EHR, including the medication schedule and test results (intervention). Patients, clinicians, and nurses completed surveys before and after the intervention. The survey evaluated the domains of caregiver workload, patient confusion and worry, patient empowerment, errors detected, and discharge planning. We performed the McNemar test to analyze binary data between paired responses on surveys for all 3 groups. Results | Participants completing the preintervention and postintervention surveys included all 50 patients (response rate, 100%), 28 of 30 clinicians (response rate, 93%), and 14 of 16 nurses (response rate, 88%). Demographics and baseline opinions about technology are shown in Table 1. Mean patient portal use was 15.6 (SD, 16.2; median, 11.2; range, 0.3-86.8) clicks per day, and time logged on ranged from 2 to 1331 minutes. We did not assess the use of the tablet for other purposes or by other users. Table 2 shows the preintervention and postintervention survey results. Thirty-three of 42 clinicians and nurses (79%) were concerned that giving patients immediate access to their test results would increase their workload, but this sentiment decreased in both groups after the intervention. Concerns that seeing test results would cause patient worry were high among clinicians and nurses (24 of 28 [86%] and 13 of 14 [93%], respectively) and greater than among patients before the intervention, but these concerns decreased in all groups. Most patients endorsed empowerment items, including control, understanding, reassurance, and following recommendations both before and after the intervention. Clinicians (25 of 26 [96%]) and nurses (13 of 14 [93%]) were more optimistic than patients (22 of 50 [44%]) that patient access to their medication lists would help them find errors, and this optimism decreased significantly across all groups after the intervention (patients, 3 of 50 [−38%; P < .001]; clinicians, 17 of 26 [−31%; P = .008]; and nurses, 7 of 14 [−43%; P = .03]). Before the intervention, 33 of 49 patients (67%) indicated that they would better understand when they would be discharged; after the intervention, the number of patients endorsing this item fell significantly (to 12 of 49 [−43%; P < .001]). Discussion | The suspected risks of giving inpatients direct access to their EHR did not bear out, with no increase in workload reported by the nurses or the clinicians and no increase in confusion or worry reported by the patients. Consistent with

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